rocketride-server

Design, test, and ship complex AI workflows from a visual canvas, right where you write code.

Drop pipelines into any Python or TypeScript app with a few lines of code, no infrastructure glue required.

Where to start

Open source, MIT. The whole engine is MIT-licensed and OSI-compliant. No enterprise edition, nothing behind a paywall.

Features

Feature	Description
Rocket Builder	Drag, connect, and configure nodes visually, no boilerplate. Real-time observability tracks token usage, LLM calls, latency, and execution. Pipelines are portable JSON, version-controllable, shareable, and runnable anywhere.
High-Performance C++ Runtime	Native multithreading purpose-built for the throughput demands of AI and data workloads. No bottlenecks, no compromises for production scale.
100+ Pipeline Nodes	15+ LLM providers, 9 vector databases, OCR, NER, PII anonymization, chunking strategies, embedding models, and more. All nodes are Python-extensible, build and publish your own.
Multi-Agent Workflows	Built-in CrewAI and LangChain support. Chain agents, share memory across pipeline runs, and manage multi-step reasoning at scale.
Coding Agent Ready	RocketRide auto-detects your coding agent: Claude, Cursor, and more. Build, modify, and deploy pipelines through natural language.
TypeScript, Python & MCP SDKs	Integrate pipelines into native apps, expose them as callable tools for AI assistants, or build programmatic workflows into your existing codebase.
Zero Dependency Headaches	Python environments, C++ toolchains, Java/Tika, and all node dependencies managed automatically. Clone, build, run, no manual setup.
One-Click Deploy	Run on Docker, on-prem, or RocketRide Cloud. Production-ready architecture from day one, not retrofitted from a demo.

Quick Start

1. Install the extension for your IDE. Search for RocketRide in the extension marketplace:

   Not seeing your IDE? Open an issue · Download directly

2. Click the RocketRide extension in your IDE

3. Deploy a server - you'll be prompted on how you want to run the server. Choose the option that fits your setup:

   • Local (Recommended) - This pulls the server directly into your IDE without any additional setup.
   • On-Premises - Run the server on your own hardware for full control and data residency. Pull the image and deploy to Docker or clone this repo and build from source.

Your first AI feature

Whether you build web apps, integrate APIs, or run backend services, you already have the mental model.

You already know	Same idea in RocketRide
A route that receives a request	A source node: webhook, chat, or dropper (file drop)
Middleware chained in order	Nodes, wired together on a canvas. Most are Python you can open and read
The response you return	A response node
Config in git, like a Dockerfile	The .pipe file: plain JSON, diffable, reviewable
Calling a service from your app	The Python or TypeScript SDK: one call in, one result out

Three steps, no API keys:

1. Run it locally. Follow the Quick Start and pick Local when asked. That's the whole install.

2. Open a working pipeline. Open examples/document-processor.pipe in your IDE. Give it a PDF or an image and it pulls out the text (including text inside images), finds names, addresses and other personal data, and returns a cleaned copy. It runs on local models, so nothing leaves your machine. Press ▶ on the source node to start it. Its source is a webhook, so it waits for input — step 3 is how you send some.

3. Call it from your code. The pipeline is now a function your service can call. Run it from the folder you opened in your IDE: the extension writes the engine's connection details into a .env there, and the SDK reads them automatically. Run it from anywhere else and the client falls back to RocketRide Cloud instead.

   pip install rocketride
   

   import asyncio
   from rocketride import RocketRideClient
   
   
   async def main():
       async with RocketRideClient() as client:
           run = await client.use(filepath='examples/document-processor.pipe')
           out = await client.send(
               run['token'], 'Alice Smith, 12 Elm St, Springfield.', objinfo={'name': 'note.txt'}, mimetype='text/plain'
           )
           print(out)
           await client.terminate(run['token'])
   
   
   asyncio.run(main())
   

   TypeScript works the same way: npm install rocketride · SDK docs

Next steps, one at a time:

This is the beginning of the README. Read the rest at the original. Read the full README ↗